3 papers
cs.CL2026
Global PIQA: Evaluating Commonsense Reasoning Across 100+ Languages and Cultures
Tyler A. Chang, Catherine Arnett, Abdelrahman Sadallah +377
To date, there exist almost no culturally-specific evaluation benchmarks for large language models (LLMs) that cover a large number of languages and cultures. In this paper, we pre…
cs.CL2026
Estonian WinoGrande Dataset: Comparative Analysis of LLM Performance on Human and Machine Translation
Marii Ojastu, Hele-Andra Kuulmets, Aleksei Dorkin +3
In this paper, we present a localized and culturally adapted Estonian translation of the test set from the widely used commonsense reasoning benchmark, WinoGrande. We detail the tr…
cs.CL2026
EstLLM: Enhancing Estonian Capabilities in Multilingual LLMs via Continued Pretraining and Post-Training
Aleksei Dorkin, Taido Purason, Emil Kalbaliyev +7
Large language models (LLMs) are predominantly trained on English-centric data, resulting in uneven performance for smaller languages. We study whether continued pretraining (CPT)…